Various domains are often associated with their own data structure, data sources, and ontology definitions related to the data and other aspects. A domain may include heterogenous data such as unstructured data that may include text, files, and documents stored in various computers and structured data that may be defined by various schemas in one or more databases. It is challenging to process a large amount of data that could be distributed among various heterogenous sources that are not easily identified and managed. Conventional techniques available for processing text and documents involve labor intensive data generation techniques such as manual identification and categorization of objects and attributes in the text and documents.
The conversion of unstructured files and documents to structured data that is organized in a manner easily accessible by a domain may often be too costly to perform consistently to capture all potential changes in unstructured files. For example, a domain may generate a large number of documents and files every second. Conventionally, while the existence or creation of those files may be captured by the domain, information included in the unstructured documents and files may contain important data that are not often automatically converted to a format that is easily accessible from a database. Also, even if some of the information is converted to structured data, data from various sources is often not sufficiently linked to provide meaningful insights regarding the domain.